The technology disclosed herein generally relates to methods and apparatus for detecting and classifying repetitive signals.
A receiver system is any system configured to receive energy waves and process these energy waves to identify desired information carried in the energy waves. As used herein, an “energy wave” is a disturbance that propagates through at least one medium while carrying energy. For examples, energy waves may comprise electromagnetic waves, radio waves, microwaves, sound waves or ultrasound waves.
Typically, a receiver system includes a transducer and a receiver. A transducer may be any device configured to convert one type of energy into another type of energy. The transducers used in a receiver system are typically configured to receive energy waves and convert these energy waves into an electrical signal. An antenna is one example of a transducer. A receiver processes the electrical signal generated by a transducer to obtain desired information from the electrical signal. The desired information includes information about signals carried in the energy waves.
Oftentimes, energy waves are used to carry repetitive signals. A repetitive signal is a signal that has a time period over which some aspect of the signal repeats. Repetitive signals are used in timing operations, synchronization operations, radar operations, sonar operations, and other suitable operations. For example, the characteristics of a repetitive signal may be used to synchronize two or more devices.
In some situations, a receiver system may receive energy waves carrying a repetitive signal but may be unable to identify desired information about the repetitive signal. For example, the receiver system may be unable to detect and/or classify the repetitive signal. Classifying a repetitive signal may include identifying one or more of the following exemplary parameters: frequency, pulse width, type of modulation, period, phase, and/or other suitable characteristics of the repetitive signal.
In at least some known signal processing systems, a plurality of mixed signals (e.g., radar signals) are received by a sensor communicatively coupled to a plurality of blind source separation (BSS) filters. Using signal processing techniques, the BSS filters are configured to separate and identify repetitive signals of interest from the plurality of mixed signals. To improve performance, at least some known BSS filters use pipelining and paralleling techniques.
Blind source separation subsystems typically have a set of filters (often instantiated as a set of circuits in a field-programmable gate array or application-specific integrated circuit, but can also be instantiated via software on a general purpose processor or digital signal processor) that is limited in processing ability. These filters typically have two main parameters, center frequency and bandwidth. There are other parameters, such as number of filter zeros and poles, but these are often fixed. The problem is to take a fixed set of tunable filters with two parameters (center frequency and bandwidth) and separate as many signals of interest (SOI) as possible from a wideband signal collection in real time given the limited filter resources.
Existing solutions typically base the choice of center frequency and bandwidth on the current highest unprocessed signal energy that is detected and may use bandwidth only in a few specific values if energy is present in an adjoining filter. This may lead to inefficient use of such hardware and missing pulsed signals. In addition, the channel that outputs a particular signal is not necessarily consistent from one pulse to another.
It would be desirable to provide enhanced systems and methods for scheduling the fixed filter resources of a blind source separation subsystem.